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rucv 1 year ago
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import torch
from tqdm import tqdm
from opt import opt
from utils.metrics import evaluate
import datasets
from torch.utils.data import DataLoader
from utils.comm import generate_model
from utils.metrics import Metrics


def test():
print('loading data......')
test_data = getattr(datasets, opt.dataset)(opt.root, opt.test_data_dir, mode='test')
test_dataloader = DataLoader(test_data, batch_size=1, shuffle=False, num_workers=opt.num_workers)
total_batch = int(len(test_data) / 1)
model = generate_model(opt)

model.eval()

# metrics_logger initialization
metrics = Metrics(['recall', 'specificity', 'precision', 'F1', 'F2',
'ACC_overall', 'IoU_poly', 'IoU_bg', 'IoU_mean', 'Dice'])

with torch.no_grad():
bar = tqdm(enumerate(test_dataloader), total=total_batch)
for i, data in bar:
img, gt = data['image'], data['label']

if opt.use_gpu:
img = img.cuda()
gt = gt.cuda()

output = model(img)
_recall, _specificity, _precision, _F1, _F2, \
_ACC_overall, _IoU_poly, _IoU_bg, _IoU_mean, _Dice = evaluate(output, gt, 0.5)

metrics.update(recall= _recall, specificity= _specificity, precision= _precision,
F1= _F1, F2= _F2, ACC_overall= _ACC_overall, IoU_poly= _IoU_poly,
IoU_bg= _IoU_bg, IoU_mean= _IoU_mean, Dice = _Dice
)

metrics_result = metrics.mean(total_batch)

print("Test Result:")
print('recall: %.4f, specificity: %.4f, precision: %.4f, F1: %.4f, F2: %.4f, '
'ACC_overall: %.4f, IoU_poly: %.4f, IoU_bg: %.4f, IoU_mean: %.4f, IOU_Dice:%.4f'
% (metrics_result['recall'], metrics_result['specificity'], metrics_result['precision'],
metrics_result['F1'], metrics_result['F2'], metrics_result['ACC_overall'],
metrics_result['IoU_poly'], metrics_result['IoU_bg'], metrics_result['IoU_mean'], metrics_result['Dice']))


if __name__ == '__main__':

if opt.mode == 'test':
print('--- PolypSeg Test---')
test()

print('Done')

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